PREDICT: Parallel Resources for Early Detection of Immediate Causes of Tsunamis

With this project we propose a re-thinking of ICT infrastructure to include a framework that exploits commodity many-core systems to evaluate models. The framework permits comparison, evaluation and improvement of competing and complementary models and appear to hold promise. Our proposal focuses on the computationally intensive tasks associated with near-field Tsunami detection, leveraging parallelism to process environmental conditions in real-time to deliver informed warnings to populations at risk. By keeping a human-in-the-loop, we include new services that support crowd-sourcing within the framework, allowing integration of sensor data with media-rich voluntary participant input. Monte Carlo simulations of relevant ocean models highlight necessary precursors and likelihood of potential threats.

Faculty Supervisor:

Drs. Yvonne Coady & Aaron Gulliver

Student:

Yanyan Zhuang/Josh Erickson/Hannan Lohrasbipeydeh

Partner:

Barrodale Computing Services

Discipline:

Computer science

Sector:

Environmental industry

University:

University of Victoria